https://azure.microsoft.com/blog/enabling-collaborative-bot-development-across-your-organization-for-any-user/This post was co-authored by Omar Aftab, Partner Director of Program Management, Power Virtual Agents. Conversational artificial intelligence (AI) is enabling organizations to improve their business in areas like customer service and employee engagement by automating some of the most READ MORE
This post is co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services.
In an age where low-latency and data security can be the lifeblood of an organization, containers make it possible for enterprises to meet these needs when harnessing artificial intelligence (AI).
Since introducing Azure Cognitive Services in containers this time last year, businesses across industries have unlocked new productivity gains and insights. The combination of both the most comprehensive set of domain-specific AI services in the market and containers enables enterprises to apply AI to more scenarios with Azure than with any other major cloud provider. Organizations ranging from healthcare to financial services have transformed their processes and customer experiences as a result.
These are some of the highlights from the past year:
Employing anomaly detection for predictive maintenance
Airbus Defense and Space, one of the world’s largest aerospace and defense companies, has tested Azure Cognitive Services in containers for developing a proof of concept in predictive maintenance. The company runs Anomaly Detector for immediately spotting unusual behavior in voltage levels to mitigate unexpected downtime. By employing advanced anomaly detection in containers without further burdening the data scientist team, Airbus can scale this critical capability across
Multi-language speech transcription was recently introduced into Microsoft Video Indexer at the International Broadcasters Conference (IBC). It is available as a preview capability and customers can already start experiencing it in our portal. More details on all our IBC2019 enhancements can be found here.
Multi-language videos are common media assets in the globalization context, global political summits, economic forums, and sport press conferences are examples of venues where speakers use their native language to convey their own statements. Those videos pose a unique challenge for companies that need to provide automatic transcription for video archives of large volumes. Automatic transcription technologies expect users to explicitly determine the video language in advance to convert speech to text. This manual step becomes a scalability obstacle when transcribing multi-language content as one would have to manually tag audio segments with the appropriate language.
Microsoft Video Indexer provides a unique capability of automatic spoken language identification for multi-language content. This solution allows users to easily transcribe multi-language content without going through tedious manual preparation steps before triggering it. By that, it can save anyone with large archive of videos both time and money, and enable discoverability and accessibility scenarios.
Multi-language audio transcription in Video
Organizations face challenges when it comes to extracting insights, finding meaning, and uncovering new opportunities in the vast troves of content at their disposal. In fact, 82 percent of organizations surveyed in the latest Harvard Business Review (HBR) Analytic Services report say that exploring and understanding their content in a timely manner is a significant challenge. This is exacerbated because content is not only spread over multiple systems but also in multiple formats such as PDF, JPEG, spreadsheets, and audio files.
The first wave of artificial intelligence (AI) was designed for narrow applications, training a single model to address a specific task such as handwriting recognition. What’s been challenging, however, is that these models individually can’t capture all the different attributes hidden in various types of content. This means developers must painfully stitch together disparate components to fully understand their content.
Instead, organizations need a solution that spans vision, speech, and language to fully unlock insights from all content types. We are heavily investing in this new category of AI, called knowledge mining, to enable organizations to maximize the value of their content.
Knowledge mining with Azure Cognitive Search
Organizations can take advantage of knowledge mining today with Azure Cognitive
https://azure.microsoft.com/blog/pytorch-on-azure-with-streamlined-ml-lifecycle/It’s exciting to see the Pytorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced. At READ MORE
Over the past few years, we have seen many examples of organizations applying conversational AI in meaningful ways. Accenture and Caesars Entertainment are making their employees more productive with enterprise bots. UPS and Asiana Airlines are using bots to deliver better customer service. And finally, BMW and LaLiga have built their own branded voice assistants, taking control of how customers experience their brand. These are just a few of the organizations that have built conversational AI solutions with Azure AI.
This week at Microsoft Ignite, we announced updates to our products to make it easier for organizations to build robust conversational solutions, and to deploy them wherever their customers are. We are sharing some of the highlights below.
Most popular open source SDK for accelerated bot development
We announced the release of Bot Framework SDK 4.6 making it easier for developers to build enterprise-grade conversational AI experiences. Bot Framework includes a set of open source SDKs and tools for bot development, and can easily integrate with Azure Cognitive Services, enabling developers to build bots that can speak to, listen to, and understand users.
Bot Framework SDK for Microsoft Teams. Developers can build Teams bots with built-in support for Teams messaging
This post is co-authored by Tina Coll, Senior Product Marketing Manager, Azure Cognitive Services and Anny Dow, Product Marketing Manager, Azure Cognitive Services.
Azure Cognitive Services brings artificial intelligence (AI) within reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, understand, and accelerate decision-making into your apps. Enterprises have taken these pre-built and custom AI capabilities to deliver more engaging and personalized intelligent experiences. We’re continuing the momentum from Microsoft Build 2019 by making Personalizer generally available, and introducing additional advanced capabilities in Vision, Speech, and Language categories. With many advancements to share, let’s dive right in.
Personalizer: Powering rich user experiences
Winner of this year’s ‘Most Innovative Product’ award at O’Reilly’s Strata Conference, Personalizer is the only AI service on the market that makes reinforcement learning available at-scale through easy-to-use APIs. Personalizer is powered by reinforcement learning and provides developers a way to create rich, personalized experiences for users, even if they do not necessarily have deep machine learning expertise.
Giving customers what they want at any given moment is one of the biggest challenges faced by retail, media, and e-commerce businesses today. Whether it’s
There are incredible transformations happening across industries through the application of AI. We have a front row seat with customers who are successfully digitizing core business processes and creating more engaging and personalized customer experiences. With Microsoft’s AI platform, Azure AI, our vision continues to center on helping our customers innovate with purpose, using productive, enterprise-scale, secure solutions. This vision is made stronger by recent partnerships like our investment in OpenAI to develop a hardware and software platform that extends Microsoft Azure capabilities in large-scale AI systems.
Today, through a number of AI innovations, we continue making it easier for organizations to adopt and apply AI in a way that meets their needs, where ever they are in their AI journey. Product updates include new capabilities in Microsoft Azure Machine Learning that boost the productivity of developers and data scientists of all skill levels, new innovations in Microsoft Azure Cognitive Services and Microsoft Azure Bot Service to simplify the creation of AI apps and agents, and new enhancements to Azure Cognitive Search to enable the development of knowledge mining applications.
Tremendous customer momentum
We are humbled by the tremendous adoption of Azure AI. Organizations large and small have adopted Azure
This post is co-authored by Abe Omorogbe, Program Manager, Azure Machine Learning, and John Wu, Program Manager, Azure Machine Learning
Congratulations to the TensorFlow community on the release of TensorFlow 2.0! In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model. In addition to reading this blog, check out the demo discussed in more detail below, showing how you can use TensorFlow 2.0 in Azure to fine-tune a BERT (Bidirectional Encoder Representations from Transformers) model for automatically tagging questions.
TensorFlow 1.x is a powerful framework that enables practitioners to build and run deep learning models at massive scale. TensorFlow 2.0 builds on the capabilities of TensorFlow 1.x by integrating more tightly with Keras (a library for building neural networks), enabling eager mode by default, and implementing a streamlined API surface.
TensorFlow 2.0 on Azure
We’ve integrated Tensorflow 2.0 with the Azure Machine Learning service to make bringing your TensorFlow workloads into Azure as seamless as possible. Azure Machine Learning service provides an SDK that lets you write machine learning models in your preferred framework and run them on the compute target of your choice, including a
https://azure.microsoft.com/blog/automated-machine-learning-and-mlops-with-azure-machine-learning/Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository, or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on READ MORE